Qualitative diagnostic signature for pancreatic ductal adenocarcinoma based on the within-sample relative expression orderings

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10^-5). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman's rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

A novel radiomics-platelet nomogram for the prediction of gastroesophageal varices needing treatment in cirrhotic patients

BACKGROUND AND AIMS

Highly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis.

METHODS

In this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad score) was constructed with the least absolute shrinkage and selection operator algorithm and tenfold cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model.

RESULTS

The Rad score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the Rad score with platelet counts, achieving an AUC of 0.987 (95% CI 0.969-1.00), 0.973 (95% CI 0.939-1.00) and 0.947 (95% CI 0.876-1.00) in the training set, internal validation set and external validation set, respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (< 5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed.

CONCLUSIONS

In this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients.

CLINICAL TRIALS REGISTRATION

NCT04210297.

Comparing EQ-5D-3L and EQ-5D-5L performance in common cancers: suggestions for instrument choosing

PURPOSE

To compare the performance of three-level EuroQol five-dimensions (EQ-5D-3L) and five-level EuroQol five-dimensions (EQ-5D-5L) among common cancer patients in urban China.

METHODS

A hospital-based cross-sectional survey was conducted in three provinces from 2016 to 2018 in urban China. Patients with breast cancer, colorectal cancer, or lung cancer were recruited to complete the EQ-5D-3L and EQ-5D-5L questionnaires. Response distribution, discriminatory power (indicator: Shannon index [H'] and Shannon evenness index [J']), ceiling effect (the proportion of full health state), convergent validity, and health-related quality of life (HRQoL) were compared between the two instruments.

RESULTS

A total of 1802 cancer patients (breast cancer: 601, colorectal cancer: 601, lung cancer: 600) were included, with the mean age of 55.6 years. The average inconsistency rate was 4.4%. Compared with EQ-5D-3L (average: H' = 1.100, J' = 0.696), an improved discriminatory power was observed in EQ-5D-5L (H' = 1.473, J' = 0.932), especially contributing to anxiety/depression dimensions. The ceiling effect was diminished in EQ-5D-5L (26.5%) in comparison with EQ-5D-3L (34.5%) (p < 0.001), mainly reflected in the pain/discomfort and anxiety/depression dimensions. The overall utility score was 0.790 (95% CI 0.778-0.801) for EQ-5D-3L and 0.803 (0.790-0.816) for EQ-5D-5L (p < 0.001). A similar pattern was also observed in the detailed cancer-specific analysis.

CONCLUSIONS

With greater discriminatory power, convergent validity and lower ceiling, EQ-5D-5L may be preferable to EQ-5D-3L for the assessment of HRQoL among cancer patients. However, higher utility scores derived form EQ-5D-5L may also lead to lower QALY gains than those of 3L potentially in cost-utility studies and underestimation in the burden of disease.